PHOTOGRAPH TAKING AND MUSIC

Transcription

1 Association for Information Systems AIS Electronic Library (AISeL) PACIS 2016 Proceedings Pacific Asia Conference on Information Systems (PACIS) Summer PHOTOGRAPH TAKING AND MUSIC SELECTION USING BRAINWAVE CONTROL PHOTOGRAPH TAKING AND MUSIC SELECTION USING BRAINWAVE CONTROL Wei-Yen Hsu National Chung Cheng University, Nai-En Chang National Chung Cheng University, Yi-Ting Lin National Chung Cheng University, Kuan-Ying Chen National Chung Cheng University, Chih-Xiang Hsu National Chung Cheng University, Follow this and additional works at: Recommended Citation Hsu, Wei-Yen; Chang, Nai-En; Lin, Yi-Ting; Chen, Kuan-Ying; and Hsu, Chih-Xiang, "PHOTOGRAPH TAKING AND MUSIC SELECTION USING BRAINWAVE CONTROL PHOTOGRAPH TAKING AND MUSIC SELECTION USING BRAINWAVE CONTROL" (2016). PACIS 2016 Proceedings This material is brought to you by the Pacific Asia Conference on Information Systems (PACIS) at AIS Electronic Library (AISeL). It has been accepted for inclusion in PACIS 2016 Proceedings by an authorized administrator of AIS Electronic Library (AISeL). For more information, please contact

2 PHOTOGRAPH TAKING AND MUSIC SELECTION USING BRAINWAVE CONTROL Wei-Yen Hsu*, Department of Information Management, National Chung Cheng University, Chiayi, Taiwan, (*Corresponding Author) Nai-En Chang, Department of Information Management, National Chung Cheng University, Chiayi, Taiwan, Yi-Ting Lin, Department of Information Management, National Chung Cheng University, Chiayi, Taiwan, Kuan-Ying Chen, Department of Information Management, National Chung Cheng University, Chiayi, Taiwan, Chih-Xiang Hsu, Department of Information Management, National Chung Cheng University, Chiayi, Taiwan, Abstract Smart phone has become more and more popular in our life. When we are at work, school, commute, or dining, if we are free, we tend to use it. In future, we will no longer need to use our hands but our brain to control the phones to do something, such as to take a picture. Instead, we use the brainwaves for control. In this study, we implement the control of the most commonly used functions of smart phones by brainwaves, such as taking pictures and listening to music. In order to achieve the objective of practicality in the wireless and remote control of smart phones, the control instructions of brainwaves are analyzed and then transferred via Bluetooth to the smartphone by wearing a wireless and portable athletic EEG devices. The experimental results indicate that the proposed system achieves the promising accuracy, and it can be further expanded to the applications of other fields, such as driving, simple instructions in real life. Keywords: brainwave control, wireless, portable, EEG

3 1. INTRODUCTION As technology advances, the features of smartphone are also more progress. Five years ago, the largest corporation of mobile phone was Nokia, and smartphone was not just used for talking that can be used to listen to music and take pictures. However, smartphone could not replace Walkman and camera at the time. Time flies, the biggest smartphone factory has changed. Apple is the largest mobile corporation who has announced their latest products, which is iphone6s. Its functionality has been very strong, whether it is also used to listen to music or take pictures or even else more unexpected features. Five years ago we could not imagine these changes. Now smartphone has big capacity, Small size, excellent sound quality and Rich features of music apps. Walkman almost non-existent is completely replaced by smartphone. The camera is almost substituted smartphone, because the average user uses smartphone to take pictures is enough. The pictures from smartphone are clear and enhancing photos immediately. You can also instantly upload to social networking sites to share with friends. Mobile has integrated into our lives and brings a variety of convenience, but we will always have a condition of not being able to touch it. For example: Taking pictures for a tons of people, in order to make sure everyone are in the picture, we may not to press the shutter and take the picture at the same time. In respect of driving, some people are used to listening to music while driving in order to ease driving-fatigued, but once you want to change the music, due to the fear of car accident, you could only continue listening. To improve these circumstances, we believe that if we could use our mobile without touching them, and we can solve these problems. Finally, we find the way which is answer-brainwave. Linking mobile to Brainwave, it can detect and transfer brainwaves to quantity through the conversion function. As a result, it will calculate a number of the level of concentration and contemplation. At this time, when you want to take pictures with a crowd of people, you can put your phone at a distance, and concentrate for seconds at specific level. A nice picture is finished. As a matter of fact, the function of changing music while driving a car works in the same way. Changing the music by concentrating to a certain level can lead to a result of not touching the phone but carry out with a more safety way. 1.1 Literature review Brain is the most mysterious areas of the body in the past, but with the advances in the science of brain, the activity of cranial nerves can be obtained by using electrophysiological methods to detect brain waves. In 1875, Richard Carton recorded electric wave from the surface of the cerebral cortex of rabbits for the first time. Later, he found that stimulating the body of an animal can make changes in brain waves. Taking advantage of this change to study the relationship between the parts of the body and areas of the cerebral cortex and to explore the function of cerebral cortex has become the basis for

4 later development of evoked potential in neurodiagnosis. Hans Berger recorded the same changes in the human skull in Haas (2003) published the record of human brain waves for the first time, and named for Electroencephalogram (EEG) to describe the change of the human brain, and did many experiments and research to find out some regularity and characteristics of brain waves. In 1934, Adrian and Matthews confirmed the notion, the concept of human brain was set great store by the world, and the study of EEG also began to grow. Brainwave instrument, belonging to non-invasive examination, is an instrument which amplifies the variation of electric field caused by the activity of brain cells and record from scalp electrodes. Its record plan of brain cells activity is displayed in wave-type. After starting to describe the discovery of human brain wave activity, Berger created a landmark in people's physical and psychological development. Because the EEG can accurately record the changes of the electric wave during the brain activity, it can obtain a more objective observation index for the study of human thinking and cognitive behavior. Zhao Lun (2004) pointed out that EEG has the advantages of noninvasive, simple equipment, good environmental adaptability, so its application scope is increasing day by day. Wise (1995) found that there is no single brainwave state in normal people. Instead, four types of brain waves would combine into a conscious organism with different time, situation and proportion, then form the individual s internal and external behavior and learning performance. Generally speaking, the types of the brain wave analysis are commonly divided into two parts, Frequency Domain Analysis and Time Domain Analysis. This study selects the Frequency Domain Analysis techniques because one kind of brain wave frequency corresponds to one kind of state of mind, For example: By comparison of different power in a certain frequency, it can be speculated that the difference of excited state and relaxation state. The brain mainly consists of two types of neurons and glial cells. Neurons, which are responsible for the processing of information and the conversion of chemical and electrical signals, are composed of small cells, dendrites and axons. Brain waves are the sum of the current pulse flows generated by the brain, its amplitude and frequency would obtain an average power of each frequency (μv2 /Hz) after the weighted average of the same category of signals via fast Fourier transform. Wolpaw et.al (1991) mentioned that the type of brain waves is determined by the frequency of brain waves. The record of brain wave s intensity comes from the surface of the scalp. The range of intensity from 0.5μV to 100μV and general radio frequency from 1 to 40Hz. Chen Jianyu (2004) study noted that brainwave researchers consider about the range of frequency that within about 30Hz majority generally. John, Jun and Yasuo (1992) mention brainwave frequency is an important parameter in brain function. Frequency is the numbers of regular waves appear in a second. Unit of frequency expressed in Hz, and brainwave frequency can be divided into δ, θ, α, β and γ five Latin sections. Ray et.al (1990) research indicates that each brainwave frequencies represent different mental state. If any of these frequencies are deficient or excessive, that means our mental performance likely to be affected. Delta (δ): Slow wave, frequencies below 4 Hz. This wave belongs to unconscious level. One year

5 old baby sleeps and patients with severe organ disease have a high Delta waves. It's most obvious in child's occipital lobe and frontal lobe of brain. Theta (θ): Slow wave, frequencies between 4-8 Hz. It s especially evident in deep sleep dreaming and meditation. This wave is a high level of physical condition, and it belongs to subconscious level. It s most obvious in parietal lobe and temporal lobe of brain. Many Patients with brain disease have high Theta waves. Alpha (α): Fast wave, frequency of 8-13 Hz. people will have this periodic wave in the quiet, relax body, the rest of the brain. It s a bridge between the conscious and subconscious level. Its potential is approximately 50μV, and most obvious in occipital lobe and parietal lobe of brain. Beta (β): Fast wave, frequencies above 12Hz. It has small amplitude, high frequency, but rarely higher than 50Hz. It s especially evident in awake and alert, also in logical thinking, calculations, reasoning. This wave belongs to consciousness level. Its potential is approximately 20μV, and most obvious in parietal lobe and frontal lobe of brain. Gamma (γ): high-frequency brainwaves, frequencies between 31-50Hz, it exists in waking consciousness, high-pitched excitement. In the past, it often ignores, but there are a growing number of researchers found that the relation of Gamma wave and selective attention. 1.2 Selfie products currently Due to the rise of smart phones in the recent years, phone with built-in camera pixel is getting better. And people are also increasing emphasis on beauty. The selfie, Facebook check-in, selfie sticks are raising. However, with the different kinds of manufactures, the selfie sticks cause more and more information security issue. The South Korean government recognize it as illegal communications equipment, worried Bluetooth function will cause interference on the bandwidth; and resorted to heavy penalties, it will be fined the equivalent of NT$840,000 or three years in prison. Recently, the major museums, amusement parks prohibit the use elongated selfie stick, because the surrounding tourists are affected easily. Therefore, some manufacturers leave out a new product: the smart phone base. It can recognize a human face self-timer base, also known as the selfie robot. Its disadvantage is the higher price up to NT$6,000, need go through to learn to use skillfully, and the use of distance only 3 meters, the maximum number of only up to 20 people, and inconvenient to carry. South Korean giant manufacture Samsung recently introduced a new camera, NX-Mini, compact body, thin, colorful and varied appearance, its screen can be turned 180 degrees, and also can touched. So it s quite convenient for various angles selfie. The most special design is scratch-resistant lenses, because it does not need additional lens cap to protect the lenses. This can be directly switched on the camera, so forget to remove the lens cap on the camera's dilemma will not happen again. The most outstanding feature of the camera is unique blink shooting, as long as you blink, shooting will begin from two seconds count down, and the hands do not need to press the shutter. But the disadvantage is

6 that when you use blink shot, self-timer face must immediately place at a good small frame inside, while blinking green. If unconscious blink or a lot of people taking pictures together will cause a big problem: the camera is easy to misjudge, and also flash of green light will affect the human eye. In addition, its price is up to NT $20,000. These products are made to solve the needs of the modern selfie. But those ignore why people take a selfie and why phone s front camera can t satisfy those who enjoy taking pictures. Now is the era of the Internet community, people take a selfie timely and share their life with their friends, maybe some delicious food, beautiful scenery, or a record of life. The most important thing is sharing and convenience. Selfie stick and selfie robot are lack of convenience, to take a picture, you have to bring it on your body when you go out and play. It is a very inconvenient thing. NX-Mini is a very strong performance camera. It has advantage of taking picture more wisely could solve the problem that the pictures are too fuzzy, which we took by the mobile s low-pixel front camera before. But ignore the different between selfie and professional photograph. In order to shoot better photos, photographers are willing to spend big money to buy a better performance, more powerful camera. But most people do? Selfie is just an act of sharing life. Most people will not want to spend so much money to buy a good function camera just to selfie. When have a performance camera, you must to spend time to learn camera use and maintenance. As long as there is a certain quality pixel can face shoot well, that the smart phone s camera function is sufficient for them. Some smart phone of flagship high price is also built-in smiling or blinking shutter, its lens can automatically detect a human face. When it determines face smiles, the camera will automatically focus and take a picture. This function is very convenient, but the price of the brand's flagship phone is too expensive to afford for everyone can. So in order to solve these problems, we innovate the use of brain waves. Only we wear brainwave instrument within Bluetooth range detectable using brainwave signals blink released, start the phone shutter, the camera becomes to your brain s content. The innovation is not image analysis, so when many people take pictures and frame limit will not be a misjudgment. Effective detection range of Bluetooth is five to ten meters, in this distance, captured 20 to 30 people is definitely not difficult thing in one shoot. Sporty brainwave instrument also broke the general impression for its strange shape. Sports brainwave instrument looks like a headband, simple shapes without heavy. Just a little redesign can become very fashionable and its focuses on convenience, space, easy to carry. With the development of brainwave technology, its price will become increasingly cheap. A set of brainwave instrument cost NT$3,000 cheaper than selfie robot and NX-Mini prices a lot. We believed brainwave instrument with blink shoot camera is the future trend, brainwave science and technology will be the focus of future development.

7 Product Self-stick Smartphone Samsung Brainwave Brand flagship dock NX Mini Smart Switch phone's Camera built-smile (wink) Self Price(NT) $100~500 $6000 $20000 $3000 $10000~30000 safety South Korean security government identified as illegal equipment Convenience It s too long to carry, and many attractions prohibited. It s too bulky to carry. For the crowd is not convenient. Easy to carry, well packed, well fold It don t need you to wear any device. ease of use simple More complex, you need to go through to learn, familiar. simple distance 1M 3M 1M 10M 1M Table 1. Brain switch compared with other devices. 1.3 Contributions We propose an innovative app for smart phone to take pictures and switch songs used with brainwave instrument. In our app, brainwave instrument through conversion functions will transmit the brain wave data to smart phone, so shutter and songs change don t need hands. Only just focus and reach the threshold, then blink, app can automatic execution easily. In setting of the threshold, the first thing we look for the best value by attention experiment. Second, we verify the difference between normal blink and self-blink by blink experiment. Brainwave amplitude of each user's neither the same law, so we will record brain waves at the same time, it is determined whether the threshold need to be adjust, carry out minor tweaks to enhance the user experience. 2. SYSTEM DESIGN 2.1 EEG Signal Acquisition and transform It s usually quite weak for EEG physiological potential, it is between about 5 ~ 30uV, the frequency of the AC signal is part of the 0.5 ~ 60Hz. Brainwaves signals can be divided into the following five different frequency bands from low to high, each having a corresponding meaning. We use the Neurosky chip, it taken 512Hz sampling rate. It can pass the original brainwave signal: alpha, beta, delta, gamma, theta and EMG for below 50Hz frequencies. By using ThinkGear TM chip, in the brainwave Instrument, we could make the EEG signal acquisition, filtering, amplification, conversion, analysis and other digital signal processing functions.

8 Attention and Meditation algorithms are the application of Fourier transform, Meditation value accounting is to rely on Alpha wave ratio, the higher the ratio, the higher its value. In the same way, Attention is calculated by the ratio of Beta waves. The Attention and Meditation are divided into 100 levels, and make a normalization process. If not normalized, it may be 200 in first second, the next second is 9900, and then the next second is 10. For users, it is difficult to understand the relative value of the relationship. Figure 1. Student wears a brainwave instrument. 2.2 System function and flow Users wear a brainwave instrument, and open the app to use the camera or music function. When users entering one of the functions, app will start recording and instantly displays the user brainwaves data. As long as fulfillment of the conditions set, app will trigger the shutter or switch next song. Another function, monitor, it allows users to monitor their focus on the case, once the attention below the value set for themselves, app will be issued a warning to remind users lack of concentration. This practical function is very useful for fatigue driving, or study. Figure 2. System flow chart.

9 3. EXPERIMENT AND RECOMMENDATIONS REQUESTED We design two experiments that attention and blink to test the situation what would be more convenience and experiential for changing songs and taking pictures. We found ten students from National Chung Cheng University Department of Information Management as subjects, and confirmed that they maintain a regular routine last night. With a view to avoid impacting the experiment result, the experimenter was required to felt asleep between 12p.m. to 8a.m., and forbidden from having caffeinated and alcoholic beverages. 3.1 Attention test experiment We measure attention of ten sample in the normal state of relaxation, and record average within a minute. Sample Average attention Table 2. Average attention (Under normal relaxed state). From table 2, there will be found Average attention approximate between 30 and 50 in the relaxed state. It means that brainwave instruction received the values will be in this range under normal relaxed state. Then, we want to know attention would be significantly different if subjects pay attention in two situation, normal relaxed and focus hard state, and observe that Average valves in three seconds and five seconds would be significantly different. Let ten subjects experiment the attention ten times in every three seconds and five seconds. We can get the average of attention in three seconds and five seconds. Sample In 3 seconds In 5 seconds Table 3. Average attention (Under focus hard state) From table 3, we found the attention in three seconds is higher than in five seconds under focus hard state. In comparison with the state of relaxation, there is a much significant difference. But during the experiment, after concentrating their attention lasts three seconds, the subjects want to recover the state of relaxation is more difficult. On the other hand, the subjects in five seconds have better difference between concentrate their attention and state of relaxation. 3.2 Blink test experiment We want to know whether brainwave amplitude would be significant differences in the way of normal blink and hard self-blink. These data helps us determine whether brainwave instrument happen misjudgment. For example, when subjects blink, EEG does not detect or when subjects do not blink,

10 EEG detect. Measure ten subject s times of normal blink and the average of brainwave amplitude in one minute, then measuring ten times of self-blink and the average of brainwave amplitude. After an experiment two we get the following data: Sample Normal blink (times) EEG determination (times) Accuracy (%) 15% 11% 28.5% 33% 9% 10% 13% 13% 12% 9% average amplitude Table 4. Brainwave instrument judgment result under normal blink Sample Self-blink (times) 10 times EEG determination (times) Accuracy (%) 50% 70% 90% 60% 60% 70% 60% 80% 100% 90% average amplitude Table 5. Brainwave instrument judgment result under self-blink From table 4 and 5, we found Normal blink less to be detected by EEG, and brainwave amplitude is low and stable. Although hard self-blink is easily detected with higher accuracy rate by EEG, the amplitude of brainwave is similar with normal blink. So the amplitude of brainwave can t make a determinant whether is self-blink. However the average accuracy of self-blink up to 73% by brainwave instrument judgment, we decide no additional thresholds to set. 3.3 Recommendations requested Based on the results of the attention and blink experiment, we order to improve user experience and innovation, design an experiment three. We want the subjects conduct self-blink under concentrated situation. If the process is in very short time (a few seconds), then this may be a less likely misjudged design. If it takes too long, the design is not suitable in practical applications. The average attention in three seconds is obtained by the attention experiment. Subjects were required to achieve their own average attention, then self-blink. Record the time this process takes. After an experiment three, we get the following data: Sample Average attention(in 3 seconds) Spend time (seconds) Table 6. Spend time that up to average attention. It s not exactly different that user s change of brainwave, the time reach average attention will be different. Therefore, the average attention that triggers shutter and next song should be varied. So a

11 preset threshold: (1) There is average attention of samples in three seconds, is average attention of samples under normal relaxed state. From this formula, we can calculate that our preset threshold is 56 after rounding. App will record the user's real-time brainwave data in run, and dynamic adjusts threshold according to change of attention. It allows users to switch songs and take a picture can easier, comfortable. 4. DISCUSSION Our app use brainwave instruments to develop innovation. We can start some functions of the smart phone with attention and blink. Brainwave instruments can determine attention with esense TM algorithm which will amplify and analyze brain waves, then calculate the value. Determination of self-blink by brainwave instruments automatically. Next, we start brain switch in situation which reach preset attention threshold and determined self-blink. After experiment and modification, we get the preset attention threshold, and provide dynamic adjustment for users. Additionally, we confirm that self-blink will be judged by the brainwave instrument. Although our app provides users with an innovative experience for camera and music player, there are many restrictions. In the past, because the brainwave instrument easy to carry, the appearance of heavy, must cover the entire skull. With the scientific and technological progress, brainwave instrument become more lightweight than a smart phone, and also more portable. People s health conscious also gradually raise from the past concerns the long-term health of the body, to today's immediate physical and mental integrity. At present, our sporty brainwave have three channel must close to the forehead to detect the brainwave and transmitted to the brainwave data in the smart phone app instantly. If channel without good access to the forehead, it will be poor reception. Further, the above experiments are all non-interference in the normal situation. However, people always will encounter many unexpected situations in everyday life, it affects brainwaves change. Therefore, sharply brainwave changes may lead to app inevitable produce false positives. On the other hand, brainwave instrument regarded as a prominent full shape in appearance. We can integrate only shape of brainwave instrument as possible into our lives. In appearance, it can minimize obtrusive brainwave instrument in visual, resulting in a negative impact. So it can be made into hair accessories, hair bands, scarves, caps and others, provide a variety of customized design and reduce impact of brainwave instrument in appearance. Although this app highly dependents on brainwave instrument, we believe that the future will be the era of mind control and brainwave instruments will become smaller. When brainwave control into our lives, we will have more software applications. Now, our app can do focus on taking pictures and blink to switch songs, primarily used inconvenient to use of mobile phones case, the future direction of development will focus on more features and more convenient user experience. In hardware terms, we

12 will continue to study how to let the brainwave instrument becomes more lightweight, in order to improve portability, and reduce costs, so that everyone can easily have a brainwave instrument. We propose several development directions in the future: Morning alarm clock: Most people have experience of couldn t get up. Through real-time monitoring the state of brainwave, the alarm clock application will keep ringing until the state of user s Beta wave appear, which means the clock won t stop until the user awake. Students learn: Students can know whether attention enough instantly or has reached a state of exhaustion in need of rest. This app can increase their study efficiency. Teaching: In order to improve education efficiency and make adjustment to the pace of teaching timely, the performance of the students brave wave and the level of the concentration will be transported to the teacher s mobile application instantly. Fatigue driving: This is very dangerous things when driving in fatigue. The app provides real-time monitoring, the fatigue will be given a warning, in order to reduce accidents. Sleep brainwave: Because nowadays people have poor sleeping quality, the mobile application will monitor the meditation of brainwave before sleeping. At the same time, the app will automatically pause and out of service until the meditation reach a specific level by broadcasting the soothing music. Despite there are still a restrictions for brainwave instrument today, it s closely to our daily life. Therefore, we look forward this app can take the first step in the brainwaves used in daily life, look to the future of unlimited possibilities. Acknowledgments The authors would like to express their sincere appreciation for grant partially from MOST H MY2, Ministry of Science and Technology, Taiwan. References Bo Zhao Guo. (2010). Integration of the human brain and the computer: the brain-computer interface Profile, National Yang Ming University institute of brain science. Cheng-Chieh Li. (2008). Applying EMD Method to Remove EEG of Eye Blink Artifacts in Measuring Fatigue State, National Taiwan University institute of mechanical engineering master s thesis. Chun-Jung Tseng, Jun Rong Ceng. (2010). Development of Car Drivers Fatigue Detection System, National Taiwan University institute of mechanical engineering master s thesis. Genaro Rebolledo-Mendez, Sara de Freitas, Attention modeling using inputs from a Brain Computer Interface and user generated data in Second Life.

13 Genaro Rebolledo-Mendez, Ian Dunwell, Erika A. Martínez-Mirón,María Dolores Vargas-Cerdán, Sara de Freitas, Fotis Liarokapis,and Alma R. García-Gaona, Assessing NeuroSky s Usability to Detect Attention Levels in an Assessment Exercise, P Haas, L. F., (2003), Hans Berger ( ), Richard Caton ( ), and electroencephalography, Journal of Neurology Neurosurgery and Psychiatry, 74 (1):90. Hsuan-Chin Chu. (2013). Human Attention Recognition Using EEG Signal, National Pingtung University of Science and Technology department of information management, IJAIT, Vol. 7, No.2. John, S. M., Jun, K. and Yasuo, T. (1992) White matter lesion in the elderly. Journal of the Neurological Sciences, 110(1-2) Jhen-Ru Lin, ICA-Based Embedded Wireless BCI System for Real-Time Drowsiness Detection, National Chiao Tung University institute of electrical control engineering master s thesis. Kun Xian Chen, Shu Jun He. (2013). application in various fields for EEG research, National Kaohsiung Normal University institute of information and computer education, TANET. Lin, Bor-Harn. (1997). Quantification of WT Coefficient Distribution for EEG Signals, National Chiao Tung University electrical and computer engineering master s thesis. Li, X., Hu, B., Zhu, T., Yan, J., Zheng, F.. (2009). Towards affective learning with an EEG feedback approach, MTDL '09 Proceedings of the first ACM international workshop on Multimedia technologies for distance learning, pp Li, X., Zhao, Q., Li, L., Peng, H., Qi, Y., Mao, C., Fang, Z., Liu, Q. and Hu, B. (2010). Improve Affective Learning with EEG Approach, Journal of Computing and Informatics, formerly: Computers and Artificial Intelligence., Vol. 29, pp Ray, W. J., Cacioppo, In J. and Tassinary, L. (Eds.). (1990). The electrocortical system. Principles of Psychophysiology. Cambridge, New York: Cambridge University Press. Shang-Ming Huang. (2012). EEG-Based Medical Assistant Systems, National Chin-Yi University of Technology institute of computer science and information engineering master s thesis. Wun-Siang Jhong, Wen Xiang Zhong. (2011). Feature Analysis for Motor Imagery-Based Brain-Computer Interface, Chung Yuan Christian University institute of mechanical engineering master s thesis. Wise, A. (1995). The high-performance mind: Mastering brainwaves for insight, healing, and creativity, New York: Putnam. Wolpaw, J., Mc Farland, D., Neat, G., and Forneris, C., 1991, An EEG-based brain-computer interface for cursor control, Electroencephalography and clinical neurophysiology, 78 (3), pp Yoshitsugu Yasui. (2009). A Brain Signal Measurement and Data Processing Technique For Daily Life.

14 Yung-Hwa Kao, Yong Hua Gao. (2013). Analysis of Emotional EEG and Development of Brain-Computer Interface, Chung Yuan Christian University institute of mechanical engineering master s thesis. Zhao Lun. (2004). ERP experiment tutorials, Tianjin Academy of Social Sciences, China.